Process controller for controlling a process to a target state

ABSTRACT

A process controller having a setting section for giving a target value for a fixed period of time, a control section including a predicting section for predicting a process response for the fixed time period in accordance with a current value of a process control amount and a virtual value of an operation amount; a quantitating section for quantitating a difference between the target value and the result by the predicting section; a calculating section for calculating a basic signal of the operation amount in accordance with the quantitated result, and an optimizing section including a predicting section for predicting a process response for the fixed time period with a control amount and an initial operation amount of the process; another quantitating section for quantitating a difference between the prediction and the target value, a calculating section for calculating the operation amount in accordance with the second quantitated result; and a section for performing control in such a way that the quantitating result and a predetermined evaluation criterion are compared with each other to perform an evaluation. The calculated result of the operation amount is repeatedly applied to the predicting section until the evaluation criterion is satisfied, a correcting signal used for optimizing the operation amount is obtained from a value of the operation amount when the quantitated result satisfies the criterion, and the correcting signal is added to a basic signal of the control section, and the adding result is output to the process.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a process controller and moreparticularly to a process controller which is suitable for control of aplant, such as a thermal power plant, in which a response gain of theprocess, a time constant, a dead time and the like vary in a non-linearmanner in accordance with a load level, and in which the time constantand the dead time are large.

2. Description of the Related Art

With respect to prediction control there is known that described in anarticle "Proportional-Plus-Integral Prediction Control Method", Trans.IEEE of Japan, Vol. 105-C, No. 6, June, 1985, in which a control amountafter n minutes is predicted and an operation amount is calculated onthe basis of a deviation between that actual control amount and thedesired amount after n minutes by the proportional-plus-integralprediction control.

As for the control which is considered impossible since a time constantand a dead time are large and the characteristics thereof changeconsiderable and in a non-linear manner in accordance with a load, steamtemperature control of a boiler of a thermal power plant isconventionally known.

For example, with respect to such control in which the main steamtemperature of a boiler after n minutes is predicted using the Kalmanfilter and an operation amount is calculated by theproportional-plus-integral control on the basis of a deviation betweenthat main steam temperature and the target value after n minutes,disclosure has been made in an article "Steam Temperature PredictionControl for Boiler Using the Kalman Filter", instrumentation, an extranumber, 1983, pp. 113-115 and in an article "Steam TemperaturePrediction Control for thermal Power Plant", IEEE Transaction on PowerApparatus and System, Vol. PAS-103, No. 9, September, 1984.

Further, as for such control in which the technology disclosed in theabove article entitled "Steam Temperature Prediction Control for BoilerUsing the kalman Filter" is applied, there is known an article"Improvements in Operation of Machine 2 of Niigata Port Thermal PowerStation", Thermal and Nuclear Power Generation, Vol. 41, No. 1, Jan.1990.

As for a method in which a model of a thermal power generation plant isused for correcting parameters of a control system, particularly inwhich a dynamic characteristic model of a thermal power generation plantis obtained by the autoregression method, using the resultant model,optimal values of PID control parameters are obtained by conventionaltechnique. such as the critical sensitivity method, and the results areprinted to be displayed, as described in JP-A-61-3202.

In addition thereto, with respect to a method in which the neuro-fuzzyis applied to a plant control, the following control methods are known:

As for the method in which the fuzzy inference is applied to aproportional-plus-integer 1 control system to realize an autotuningsystem, there is known an article "Autotuning System for PID Controllerto which the Fuzzy Inference is Applied", Hitachi Technical Review, Vol.71, No. 8, August, 1989.

As for the method in which when the standard starting schedule iscorrected with respect to a starting equipment of a power generationplant, the simulation by the dynamic characteristic model of the plantis performed by means of the fuzzy inference, JP-A-63-945005,JP-A-63-94007, JP-A-6394008, and the U.S. Pat. No. 4,868,754 are known.

As for the method in which an identification neural network is made tolearn the characteristics of the process, and by using a resultantmodel, the parameters of the neural network model for the optimizationsolution are determined thereby to determine the optimal operationamount, JP-A-2-161501 is known.

Hitherto, as the control method of the steam temperature of the boilerof the thermal power generation plant, the following methods are taken.

(1) As described in JP-B-64-10721 for the purpose of reducing anovershoot or an undershoot from a target value which is caused by themanipulation delay due to a large time constant and dead time of theprocess, an operation amount is maintained at the same value as in thecurrent state, a control amount after one control period is predictedusing an equation of state of the process with the current value of thecontrol amount being treated as an initial value, the equation of stateis repeatedly used with the predicted value being treated as an initialvalue, to thereby predict a deviation between a steam temperature of thefuture and a target value thereof, a proportional-plus-integralcalculation result based on the deviation is added to a fuel operationcommand signal in the form of a correction signal, and the compositesignal is treated as an operation amount signal to control the process.

(2) As described in JP-A-58-40612, a method is employed such that acontrol system is made to include therein the total system model of theprocess, and by using the control system, an optimal value of anoperation amount is searched on the basis of the mathematicalprogramming. In other words, the non-linearity is modeled in the form oftable information, or is expressed in the form of a physical formula, tothereby obtain an optimal value of the operation amount using thenon-linear planning which is typified by the complex method.

In the above-mentioned prior art, there arise the following problems

PRIOR ART METHOD I

(1) Since essentially the object of the non-linear operation is modeledin a linear manner and the modeled result is repeatedly used, there isthe possibility that a model error due to the linearization isaccumulated in the prediction error of the control amount.

(2) The deviation between the predicted value of the control amount andthe target value thereof is directly applied to an input of a linearintegrator to directly perform the calculation, and the calculatedresult is used as the operation amount signal. Therefore, there is thepossibility that the prediction error due to the model and set values ofthe control parameters of the linear integrator directly influence upona final control element, and the unnecessary disturbance may be appliedto the plant.

PRIOR ART METHOD II

The non-linear planning or the like, such as the complex method, isemployed to search of the optimal value of an operation amount so thatthe determination of the operation amount requires too much time.Therefore, there are some problems in the application of this method tothe real-time control.

SUMMARY OF THE INVENTION

It is therefore an object of the present invention to provide a processcontroller in which a response gain of the process, a time constant, adead time and the like vary in a non-linear manner in accordance with aload. It is a further object that unnecessary disturbance due to thecontrol model error and the nonconformance of set values of controlparameters of a linear integrator do not influence the process having alarge time constant and dead time, and that the controlled variable ofthe process is controlled stably and favorably to the target value.

In order to attain the above objects, the present invention comprises:

a target value setting, section for giving a control target valuebeginning with the current time:

a control section including therein a process response predictingsection for predicting a process response in the future in accordancewith a current value of a process control amount and a virtual value ofan operation amount, a prediction response quantitating section forquantitating a difference between the target value and the result by thepredicting section, and an operation amount calculating section forcalculating a basic signal of the operation amount in accordance withthe quantitating result; and

an operation amount optimizing section including therein a processresponse predicting section for predicting a process response in thefuture with a control amount and an operation amount of the processbeing treated as initial values, a prediction response quantitatingsection for quantitating a difference between the predicting result andthe target value, an operation amount calculating section forcalculating the operation amount in accordance with the quantitatingresult, and a section for performing control in such a way that thequantitating result and a predetermined evaluation criterion arecompared with each other to perform the evaluation, the calculatingresult of the operation amount is repeatedly applied as an input to theprocess response predicting section until the evaluation criterion issatisfied, an operation amount correcting signal used for optimizing theoperation amount is obtained from a value of the operation amount whenthe quantitating result satisfies the criterion, the correcting signalis added to an operation amount basic signal of the control section, andthe adding result is output to the process.

Then, in the control section, the response of the process up to thefixed period of time from the current time can be predicted, the resultof quantitating the difference between the predicted value and thetarget value given by the target setting section can be learned inadvance, and the basic signal of the operation amount used fordecreasing the difference between the target value and the predictedvalue after the fixed period of time can be obtained using thequantitating result.

In the operation amount optimizing section, the response of the processup to the fixed period of time from the current time in the case wherethe basic signal of the operation amount is outputted to the process ispredicted, the difference between the target value given by the targetsetting section and the predicted value is quantitated, and it isdetermined whether or not the result of the quantitation (e.g., thecontrol deviation after the fixed period of time) satisfies theevaluation criterion. Wherein the evaluation criterion is notsatisified, the subsequent operation amount is newly calculated usingthe subsequent quantitating result, the calculating result is applied asan input of the process response predicting section, and the aboveprocedure is repeated until the criterion is satisfied, whereby anoptimal operation amount can be obtained. The optimal operation amountsignal is used as the correction signal for the basic signal of theoperation amount in the control section, and the difference between theoptimal signal and the basic signal is input to the control section. Theoperation amount signal from the control section is output as anaddition signal which is obtained by adding the basic signal to thecorrecting signal. As a result, the operation signal corresponding tothe correcting signal is as a preceeding signal, added in advance to theprocess. Therefore, it is possible to perform optimal control withoutany influence from the unnecessary disturbance.

The value of the optimal operation amount is certified in the case wherethe prediction accuracy of the process response in the control sectionsatisfies the criterion and the control parameters of the calculatingsection of the operation amount is suitably set. However, there is thepossibility that the control characteristics of the process are largelychanged by the change or the like of the load, the difference betweenthe response predicting result of the process and the actual response ofthe process increases so as to exceed the reference value, or thenonconformance occurs in the control parameters of the operation amountcalculating section. As a result, there arises the possibility that theoptimization of the operation amount signal may not be certified.

In a parameter tuning section, the characteristic parameters of theprediction model of the process response predicting section in each ofthe control section and the operation amount optimizing section arecorrected in such a way as to decrease the deviation between the actualresponse signal of the process with respect to the operation amountsignal output from the control section and the signal output from thereal-time plant response simulator, whereby the calculating result ofthe process response predicting section is made to correspond to theresponse of the actual process. On the other hand, the controlparameters of the operation amount calculating section in each of thecontrol section and the operation amount optimizing section arecorrected in such a way that with respect to the actual response of theprocess, the response characteristics thereof are evaluated in the formof evaluation indexes such as the ratio of the settling time to thetarget time, the damping ratio and the overshoot quantity, thussatisfying the predetermined evaluation criterion.

The correcting operation of the above parameter tuning section allowsthe characteristic parameters of the prediction model in each of thecontrol section and the operation amount optimizing section to becorrected to the value in conformity with the characteristics of theprocess. As a result, it is possible to ensure the optimization of theoperation amount output from the control section.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing one embodiment of a process controlsystem of the present invention;

FIG. 2 is a block diagram showing an example in which the presentinvention is applied to main steam temperature control of a thermalpower generation plant;

FIG. 3 is a block diagram showing an example in which a neural networkand a learning method are applied to the main steam temperature controlof the thermal power generation plant;

FIG. 4 is a diagram for explaining a learning algorithm of the neuralnetwork;

FIG. 5 is a diagram showing an example in which a teacher signal of theneural network of FIG. 3 is produced by the fuzzy inference;

FIG. 6 is a graphical representation showing membership functions of thefuzzy inference of FIG. 5;

FIG. 7 is a block diagram showing an example in which the fuzzyinference is applied to model correction;

FIG. 8 is a diagram showing an embodiment of the fuzzy inference of FIG.7.

FIGS. 9A to 9C are graphical representations showing an embodiment of amethod of determining an optimal operation amount; and

FIG. 10, FIG. 11 and FIG. 12 are respectively block diagrams showingother embodiments of the present invention.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

Embodiments of the present invention will hereinafter be described indetail with reference to the accompanying drawings.

FIG. 1 is a block diagram showing an embodiment of a process controlsystem to which the present invention is applied. The present systemincludes a target value setting section 1000, a control section 2000, anoperation amount optimizing section 3000, a parameter tuning section4000, and a process 5000 which is to be an object of the control

The setting section 1000 produces a control target value 1001 for theprocess control up to the fixed period of time after the current time inthe future.

In the control section 2000, a process response predicting section 2100predicts a response value 2001 for the process control up to the fixedperiod of time after the current time. A predicted response quantitatingsection 2200 quantitates the predicted value 2001 in accordance with thepredicted value 2001 and the control target value 1001. An operationamount calculating section 2300 outputs a basic signal 2003 of anoperation amount in accordance with the quantitated result 2002 of thepredicted value. An adding section 2400 adds the basic signal 2003 andan operation amount optimizing signal 3007 to each other to output atotal operation amount signal 2005. A switching circuit 2500 switchesover the signal 2005 to either the side (a) of the process 5000 or theside (b) of the optimizing section 3000.

In the optimizing section 3000, a process response predicting section3100 receives the current total operation amount signal 2005 and aprocess control amount signal 5001 to predict a response value 3001 bythe process control up to the fixed period of time. A predicted responsequantitating section 3200 outputs a quantitated result 3002 of thepredicted response value in accordance with the predicted value 3001 andthe control target value 1001. An operation amount calculating section3300 outputs a virtual value 3003 of an operation amount in accordancewith the quantitated result 3002 of the above predicted response. Anevaluation criterion section 3400 outputs an evaluation criterion 3005used for evaluating the quantitated result 3002. A calculation controlsection 3500 outputs an output switching signal 3006 obtained from aresult of comparing the quantitated result 3002 with the evaluationcriterion 3005. A switching circuit 3600 switches over the virtual value3003 to either the side (a) of the control section 2000 or the side (b)of the predicting section 3100.

An adder 3700 subtracts a value of the basic signal 2003 from thevirtual value 3003 to output a resultant difference 3007, as anoptimization signal, to the adder 2400 of the control section 2000.

In the section 4000, a real-time model section 4100 infers the responseof the process to the total operation amount signal 2005 in a real-timemanner to output an inference signal 4001. A model error evaluatingsection 4200 quantitatively evaluates a difference between the inferencesignal 4001 and a process control amount signal 5001 to output aninferring model error 4002. A model correcting section 4300 calculatescorrected amount of characteristic parameters of a process responsepredicting model in the predicting section 2100 of the control section2000 and the predicting section 3100 of the optimizing section 3000 inaccordance with the inferring model error 4002 to correct each of thepredicting sections 2100 and predicting section 3100 using a correctingsignal 4003. A response characteristic evaluating section 4400quantitatively evaluates real response characteristics (the overshoot,the settling time and the like) of the control amount signal 5001 to thecontrol target value 1001 to output the evaluated result 4004. A controlparameter correcting section 4500 corrects control parameters of thecalculating section 2300 of the control section 2000 and the calculatingsection 3300 of the optimizing section 3000.

The description will subsequently be given with respect to the operationof the present embodiment.

In the control calculation of the section 2000, the switching circuit2500 is first switched over to the side (b), the process response up tothe fixed period of time after the current time is predicted by thepredicting section 2100, taking the current control amount signal 5001and the operation amount signal 2005 as initial values, and a deviation3002 between the predicted process response and the control target value1001 is calculated by the quantitating section 2200. The calculatingsection 2300 calculates the operation amount basic signal 2003 inaccordance with the deviation signal 2002.

On the other hand, the optimizing section 3000 switches over the switchof the switching circuit 3600 to the side (b), the predicting section3100 predicts the process response up to the fixed period of time,taking the current control amount of the signal 5001 and the operationamount of the signal 2005 as initial values, and the quantitatingsection 3200 calculates the deviation 3002 between a value of thepredicted process response 3001 and the control target value 1001. Thecalculating section 3300 outputs the virtual value 3003 in accordancewith the deviation signal 3002. On the other hand, the calculationcontrol section 3500 determines whether the deviation 3002 is smallerthan the evaluation criterion 3005, i.e., whether the evaluationcriterion 3005 is satisfied. When the evaluation criterion is satisfied,the switching circuit 3600 is switched over to the side (a) inaccordance with the switching signal 3006, so that the operation amountof signal 3007 obtained by subtracting the operation amount of the basicsignal 2003 from the virtual value 3003 in a subtracter 3700 is output.The optimized operation amount of the signal 3007 is added to theoperation amount of the basic signal 2003 by the adder 2400 of thecontrol section 2000 so that the total operation amount 2005 isobtained. The switching circuit 2500 is switched over to the side (a) tooutput the total operation amount 2005 to the process 5000 as an optimaloperation amount. On the other hand, when the deviation 3002 does notsatisfy the evaluation criterion, the switching circuit 3600 remainsswitched over to the side (b), the virtual value 3003 is changed intothe operation amount to the predicting section 3100, and the aboveprocedure is repeated until the evaluation criterion is satisfied.

The tuning section 4000, first, corrects the characteristic parametersof the predicting model and the real-time model to the predictingsections 2100 and 3100 with the correcting signal 4003, in accordancewith the difference between the inference signal 4001 of the processresponse obtained from the real-time model section 4100 and the actualresponse signal 5001, using the evaluating section 4200 and thecorrecting section 4300. Thus, the prediction accuracy of the processresponse and the real-time prediction accuracy are improved.

On the other hand, the predicting section 4400 evaluates the responsecharacteristics of the process in accordance with the relation betweenthe actual response signal 5001 of the process and a signal of thetarget value 1001, and tunes the control parameters of the calculatingsections 2300 and 3300 to optimal values in accordance with thecorrecting signal 4505 output from the correcting section 4500, in sucha way that the response characteristics are not deviated from apredetermined reference value as the characteristic change of theprocess.

FIG. 2 is a block diagram showing a configuration of an embodiment inwhich the present invention is applied to a main steam temperaturecontrol of a thermal power generation plant. In the present embodiment,fuzzy inference and a neural network are applied to a control parametercorrecting section 4500. The description will hereinbelow be given withrespect to the arrangement and operation of the present embodiment.

A control section 2000 includes a main steam temperature predictingmodel section 2100 which receives a main steam temperature 5101 from amain steam temperature transmitter 5100, and a fuel operation amount2005, as well as a process amount 5200 necessary for prediction of mainsteam temperature to predict a main steam temperature 2001 after thefixed period of time, a subtracter 2210 which calculates a deviation2002 between a target value 1001 of the main steam temperature obtainedfrom a section 1100 for setting the main steam temperature target valueand the predicted value 2001 of the main steam temperature, a linearintegrator 2310 which receives a deviation 2002 to perform aproportional-plus-integral calculation, a fuel program section 7000which converts a load demand 100 into a fuel command 7001, an adder 2310which adds the fuel command 7001 and an output from an integrator 2310to each other, a subtracter 2330 which subtracts a fuel flow amountsignal 5301 from a transmitter 5300 of a fuel flow amount from an output2321 of the adder 2320, a linear integrator 2340 which receives anoutput 2331 from the subtracter 2330 to perform aproportional-plus-integral calculation, an adder 2400 which adds anoutput 2003 from the linear integrator 2340 and an optimization signal3007 of an operation amount to each other, and a switching circuit 2500which switches over a total operation amount signal 2005 as an outputfrom the adder 2400 to either the side (a) of a fuel control valve 5400or the side (b) of an operation amount optimizing section 3000.

The optimizing section 3000 includes a section 3110 of a modelpredicting the main steam temperature which receives the main steamtemperature 5101 and the fuel operation amount 2005 as well as a processamount 5200 necessary for prediction of the main steam temperature topredict a main steam temperature 3001 after the fixed period of time,and a subtracter 3210, a linear integrator 3310, an adder 3320, asubtracter 3330 and a linear integrator 3340 which have the samefunctions of those of the subtracter 2210 the linear integrator 2310,the adder 2320, the subtracter 2330 and the linear integrator 2340 inthe control section 2000, respectively. The optimizing section 3000further includes a calculation control section 3500 which compares anoutput signal 3002 from the adder 3210 and a signal of an evaluationcriterion value 3401 output from an evaluation criterion section 3400with each other to output an output switching signal 3501, a switchingcircuit 3600 which is controlled by the signal 3501 to switch over anoutput signal 3003 from the linear integrator 3340 to either the side(a) of the control section 2000 or the side (b) of the predicting modelsection 3110, and an adder 3700 which calculates a difference betweenthe output signal 3003 and the output signal 2003 from the linearintegrator 2340 to add a resultant signal 3007 to the adder 2400.

A parameter tuning section 4000 includes a neural network 4530 whichreceives the target value 1001, a fuel command 7001, a main steamtemperature 1501, and a value 4550 of a predictive time (the value aftern minutes from the current time) to output a signal used for setting aproportional gain and an integration time constant of the linearintegrators 2310 and 3310, a section 4400 for evaluating responsecharacteristics which receives the main steam temperature 5101 and asetting value 1001 of the main steam temperature to quantitativelyevaluate the response characteristics of the main steam temperature, afuzzy reasoning section 4510 which outputs a correction amount 4511 ofthe control parameters by the fuzzy reasoning in accordance with aquantitative evaluation result 4004 and a knowledge 4521 from aknowledge base 4520, and a learning section 4540 which receives thetarget value 1001, the fuel command 7001, the main steam temperature5101 and the predictive time 4550 as learning signals and the value ofthe control parameters 4512 after the correction as a teacher signal tomake the neural network 4530 learn optimal control parameters 4512.Incidentally, the reference numeral 4541 designates a learning input ofthe neural network 4530, the reference numeral 4512 the teacher signal,and the reference numeral 4532 a reading signal of internal informationfrom the neural network.

The control operation of the present embodiment will hereinbelow bedescribed.

The linear integrator 2310 determines the correcting signal used formaking the main steam temperature follow the setting value in accordancewith the deviation between the predicted value 2001 of the main steamtemperature predicted by the predicting model section 2110 and thesetting value 1001 of the main steam temperature. The linear integrator2340 determines the control basic signal 2003 for the fuel adjustingvalve 5400 in accordance with the signal 2331 of the deviation betweenthe fuel amount signal 5301 and the total fuel command 2321, which isobtained by correcting the fuel command 7001 in accordance with thecorrecting signal 2311.

On the other hand, in the optimizing section 3000 as well, the virtualvalue 3003 of the operation amount for the fuel amount is calculated bythe same procedure. However, in the case where a value of the signal3002 of the deviation between the predicted value 3001 and the settingvalue 1001 of the main steam temperature exceeds the evaluationcriterion value 3401, the virtual value 3003 is input as the operationamount signal of the fuel amount from the predicting model 3110 (theswitching circuit 3600 is switched over to the side (b)), andthereafter, the iterative calculation is carried out. In the case wherea value of the deviation signal 3002 is smaller than the evaluationcriterion value 3401, the switching circuit 3600 is switched over to theside (a), so that the optimizing signal 3007 is output. This signal isadded to the control basic signal 2003 by the adder 2400 to generate thetotal operation amount signal 2005. After the switching circuit 3600 isswitched over to the side (a), the switching circuit 2500 is switchedover to the side (a), so that the total operation amount signal 2005 isoutput as the control signal for the fuel control value 5400.

The description will subsequently be given with respect to the operationof the parameter tuning.

The control parameters of the linear integrators 2310 and 3310 are setin accordance with the target value 1001 of the main steam temperature,the fuel command 7001, the predictive time 4550, and the main steamtemperature 5101, in response to the setting value 4531 of the controlparameter, on the basis of the contents which ar learned in advance bythe neural network 4530.

The change of the learning contents of the neural network 4530 isperformed in the following manner. First, in the response characteristicevaluating section 4400, the response characteristics of the maintemperature 5101 to the setting value 1001 are quantitated and evaluatedin accordance with the evaluation indexes such as the overshootquantity, the damping ratio and the setting time ratio. On the basis ofthe classification of the evaluated result by the membership functions,and the correcting rules stored in the knowledge base 4520, thecorrecting signal 4511 for the control parameters is determined by thefuzzy reasoning section 4510. Then, a subsequent control parametersetting signal 4512 is newly produced by adding the correcting signal4511 to a control parameter setting signal 4531 before the correction.Input to the learning section 4540 are the main steam temperature 5101,the fuel flow amount 5301, the setting value 1001, the fuel command7001, the prediction time 4550 and the evaluation indexes of the aboveresponse characteristic, and is given the above signal 4512 as theteacher signal. Thus, a new network is formed by the learning. As aresult, when the same input conditions as in the above case after thesubsequent time, the same value as that of the teacher signal will beoutput from the neural network.

Thus, with the process 5000, the operational characteristics will beimproved in a self-growth manner as the results are accumulated oneafter another (the present embodiment is improved in that the main steamtemperature follows the target value).

FIG. 3 is a view showing a neural network 4530 and the relationshipbetween a learning input signal 4541 of the network and a learningcontrol parameter (in the present embodiment, the learning controlparameters include a proportional gain setting Kp and an integrationgain setting K_(I)) setting signal 4512. The learning input signal 4541is made up of a target setting value 1100 and a control amount 5001 ofthe process. Then, by the setting value 1100 it means the setting valueof the main steam temperature, the fuel command, the overshoot quantity,the settling time ratio, the damping ratio, the prediction time and thelike. Moreover, by the control amount 5001 of the process, it means themain steam temperature, the fuel flow amount and the like. That is, thenumber of units of an input layer is, as described above, eight, and thenumber of units of an output layer is two. Moreover, the number of unitsof an intermediate layer does not need to be exactly determined andtherefore it may be arbitrary.

The reference numeral 4540 designates a learning section which includesa first learning control circuit 6001 which receives outputs of theintermediate layer to correct the weights of inputs of the output layerand a second learning control circuit 6002 which receives outputs of theinput layer to correct the weights of inputs of the intermediate layer.The concrete arrangements of the circuits 6001 and 6002 are shown inFIG. 4.

A new learning input signal 4541 and a new learning control parametercorrecting signal 4512 are respectively supplied to the input layer andthe output layer of the neural network 4530, one after another, as theplant accumulates the operational results. Thus, the synapse weight ofthe neural network will be improved by the learning section 4540 and theoperational performance of the process will be improved in a self-growthmanner.

FIG. 5 is a view showing a concrete example of a method of determiningthe correction signal 4511 for the control parameters by the fuzzyreasoning in FIG. 2. In the evaluating section 4400, the responsecharacteristics of the main steam temperature 5101 are first evaluatedwith respect to three indexes including the overshoot quantity E, thedamping ratio D and the settling time ratio R in the relationship to thetarget value 1001 of the main steam temperature. The fuzzy reasoningsection 4510 applies the evaluated values in accordance with theevaluation indexes to the membership functions shown in FIG. 6 anddetermines which classes the evaluated values belong to.

Subsequently, the rule of interest is selected from the correcting rulesof the control parameters stored in the knowledge base 4520, the resultsobtained using the above membership functions are applied to conditionsection (IF section), and the conclusion is introduced in a conclusionsection (THEN section) by the fuzzy logic calculation. That is, in thepresent example, the values of E and D are first applied to thecondition section of the rule 1 (then, put as x_(E) and x_(D)), and theadaptation ω₁ is obtained from the following equation (1).

    ω.sub.1 -min(VB(x.sub.ε), VB(x.sub.D))       (1)

In the present example, the relationship of ω₁ =VB(x_(D)) isestablished. As a result, a correction coefficient α¹ _(KP) of theproportional gain and a correction coefficient α¹ _(KI) of theintegration gain by the rule 1 are obtained in the form of values on thex-coordinate of the gravities of slanting line portions A and B in thesection 4510 of FIG. 5, respectively. Subsequently, with respect to therule 2 as well, the same inference calculation is performed. If a valueof R is put as x_(R), the adaptation ω₂ is given by

    ω.sub.2 =min(VB(x.sub.ε), VB(x.sub.D), MB(x.sub.R)) (2)

In the present example, the relationship of ω₂ =MB(x_(R)) isestablished. As a result, a correction coefficient α² _(KP) of theproportional gain and a correction coefficient α² _(KI) of theintegration gain are obtained in the form of values of the x-coordinateof the gravities of slanting line portions C and D in the section 4510of FIG. 5, respectively.

In the present example, it is assumed that only the rules 1 and 2 areapplied thereto. Thus, if, as the result of combining the calculationresults by both the rules, composite correction coefficients arerepresented by α_(kP) and α_(kI), respectively, α_(kp) is obtained inthe form of a value of the x-coordinate of the result of combining thegravities of the slanting line portions A and C in the section 4510 ofFIG. 5, while α_(kI) is Obtained in the form of a value of thex-coordinate of the result of combining the gravities of the slantingportions B and D in the section 4510 of FIG. 5.

FIG. 7 is a block diagram showing the arrangement in which a system bythe fuzzy reasoning is applied as the predicting model correctingsection 4300 of FIG. 1, by taking the main steam temperature of thethermal power generation plant shown in FIG. 2 as an example. Thepresent embodiment will hereinbelow be described. First, embodiments ofpredicting models 2110 and 3110, and a real-time model 4100 will bedescribed.

The predicting models 2110 and 3110, and the real-time model 4100 aredifferent in method of application and object of application from eachother, as already described with reference to FIG. 1 and FIG. 2.However, the arrangement of the model is essentially the same. Thus, inthe present embodiment, for brevity, there is shown an example of asecondary heater model which will hereinbelow be described.

The thermal transfer model of the secondary heater can be expressed withthe following equations, by applying the law of conservation of energythereto. ##EQU1## and V_(s) : a capacity of a pipe flow path

γ_(r) : a specific weight of an internal fluid

F_(s) : a flow rate of an internal fluid

C_(po) : a specific heat at constant pressure of steam in an outlet of asecondary heater

C_(pi) : a specific heat at constant pressure of steam in an inlet of asecondary heater

A: a thermal transfer area of a secondary heater

α_(ms) : a coefficient of thermal conductivity from a pipe to steam

θ_(so) : a steam temperature of an outlet of a secondary heater

θ_(si) : a steam temperature of an inlet of a secondary heater

θ_(m) : a pipe temperature

θ_(s) : an internal fluid temperature

C_(m) : a pipe specific heat

M_(m) : a pipe weight

α_(gm) : a coefficient of thermal conductivity from gas to a pipe

F_(f) : a flow rate of fuel

F_(a) : a flow rate of air

F_(grf) : a flow rate of recycled gas

F_(gBf) : a flow rate of gas of a boiler

The description will subsequently be given with respect to the operationof the correcting section 4300 in which the above model is an object,with reference to FIG. 7.

A model error evaluation section 4200 compares a main steam temperature5101 and a prediction result 4101 by the real-time model 4100 with eachother to evaluate them. As a result, in a fuzzy reasoning section 4310,the correction quantities for the characteristic parameters of Ts, Tg,Ks, Kg, Kf and the like which are shown in the equations (2) through (5)of the above predicting models 2110 and 3110, and the real-time model4110 are calculated using a signal 4002 and a characteristic parametercorrecting rule 4321 stored in a knowledge base 4320 by the fuzzy logic.By the resultant signals, the characteristic parameters are corrected.Thus, it is possible to correct the prediction errors of the processresponse due to the predicting models 2110 and 3110

FIG. 8 is a diagram showing a concrete example of the fuzzy reasoningmethod of the embodiment shown in FIG. 7.

In the present embodiment, a deviation between a predicted value 4101 bythe real-time model 4100 of a secondary heater outlet temperature to anoperation amount 2005 of a fuel flow amount, and an actual value 5101 isevaluated with an error of the response characteristics, i.e., portionsA and B in FIG. 8 in the model error evaluating section 4200. On thebasis of those values, a correction amount ΔTs of the characteristicparameter Ts and a correction quantity ΔKg of the characteristicparameter Kg are calculated. That is, the fuzzy reasoning section 4320applies the error of the portion A and the error signal 4002 of theportion B by the evaluating section 4200 to the membership functionsshown in FIG. 8 and determines which classes the errors belong to.

Subsequently, the rule of interest is selected from the characteristicparameter correcting rules of the characteristic parameters stored inthe knowledge base 4320, the results obtained using the above membershipfunctions are applied to a condition section (IF section), and theconclusion is introduced in a conclusion section (THEN section) by thefuzzy logic calculation. That is, in the present embodiment, the errorε_(A) of the portion A and the error ε_(B) of the portion B are firstapplied to the condition section of the rule 1, and the adaptation ω₁ isobtained from the following equation (11).

    ω.sub.1 =min(PB(ε.sub.A), NS(ε.sub.B)) (11)

In the present embodiment, since NS(ε_(s))<PB(ε_(A)) is true, therelationship of ω₁ =NS(ω_(B)) is established. As a result, correctionamounts for the time constant Ts and the gain Kg by the rule 1 areobtained in the form of values on the x-coordinate of the gravities ofslanting line portions A and B in the section 4320 of FIG. 8,respectively. With respect to the rule 2 as well, similarly, theadaptation ω₂ is given by:

    ω.sub.2 =min(PM(ε.sub.A), NM(ε.sub.B)) (12)

In the present embodiment, since PM(ε_(A))<NM(ε_(B)) is true, therelationship of ω₂ =PM(ε_(A)) is established. As a result, correctionamounts for the time constant Ts and the gain Kg by the rule 2 areobtained in the form of values on the x-coordinate of the gravities ofslanting line portions C and D in the section 4320 of FIG. 8,respectively.

In the present embodiment, it is assumed that only the rules 1 and 2 areapplied thereto. Thus, if, as the result of combining the calculatedresults by both the rules, composite correction amounts for the timeconstant Ts and the gain Kg are represented by ΔTs and ΔKg,respectively, ΔTs is obtained in the form of value of the x-coordinateof the result of combining the gravities of the slanting line portions Aand C, while ΔKg is obtained in the form of value of the x-coordinate ofthe result of combining the gravities of the slanting line portions Band D.

Incidentally, in the embodiments shown in FIG. 1 and FIG. 2, processresponse predicting sections 2100 and 3100, prediction responsequantitating sections 2200 and 3200, and operation amount calculatingsections 2300 and 3300 which are provided in the control section and anoperation amount optimizing section, respectively, are individuallyprovided. However, the control section and the process responsepredicting section are different in object of application from eachother, but are essentially the same with processing. Thus, in carryingout the present invention, even if the controller is arranged in such away that the individual processing sections are unified, and the unifiedsection is shared between the control section and the process responseprediction section, this arrangement does not depart from the object ofthe present invention.

FIG. 9 is a graphical representation showing an embodiment of a methodof determining an optimal operation amount u_(opt) when the controltarget setting value at the time u of the future is given by P. Theoperation thereof will hereinbelow be described on referring to theembodiment of FIG. 2.

First, in FIG. 9A, the control target 1001 is given in the form of curveP. In the case where the control amount 2005 in FIG. 9B is not changedfrom the current value u_(o) if the response of the control amount up tothe time (to+n·Δt) of the future is given by A, the control deviation2002 at the time of the future will be ε_(o). In the control section2000, hereinafter, an operation amount basic signal u_(B) is calculatedin accordance with the deviation ε_(o). In an optimizing section 3000,the evaluation criterion ε_(p) and the deviation ε_(o) are compared witheach other. Then, if the relationship of |ε_(o) |>ε_(p) is established,the response of the control amount up to the time of the future isobtained in the form of curve B with the signal u_(B) and the currentvalue xo of the control amount being treated as initial values. As aresult, the control deviation 2002 at the time of the future becomes ε₁. If the relationship of |ε₁ |>ε_(p) is still established, the responseof the control amount up to the time of the future is obtained in theform of curve C with u'=u_(B) +u₁ obtained by adding the operationamount u₁ and u_(B) to each other which are calculated in accordancewith ε₁, and the current value xo being treated as initial values. Ifwith respect to the control deviation ε₂, the relationship of |e₂|>ε_(p) is still established, by the same processing as in the abovecase, the operation amount u₂ is calculated. Then, the response of thecontrol amount up to the time of the future is obtained in the form ofcurve D with u₂ '=u₁ '+u₂, i.e., u₂ '=u_(B) +u₁ +u₂ and the currentvalue xo being treated as initial values. Then, if the relationship ofε₃ >ε_(p) is established, the value of u₂ '-u_(B) ' obtained bysubtracting the signal 2003 (designated u_(B) ') from u₂ ' in thesubtracter 3700 (if the signal 7001 and the like don't change from thetime of obtaining u₁ and u₂, the relationship of u_(B) '=u_(B) isestablished, while if the signal 7001 and the like change, therelationship of u_(B) ' ≠u_(B) is established) (when u_(B) '=u_(B), u₂'-u_(B) =u₁ +u₂) is applied, as an operation amount optimization signal3007, to the adder 2400 of the control section. When the relationship ofu_(B) '=u_(B) is established, the optimal operation amount u_(opt)=u_(B) +u₁ +u₂ is outputted, thus controlling the fuel control valve5400.

FIG. 10 is a block diagram showing an embodiment which is arranged insuch a way that in FIG. 1, a switch 2900 is provided before the adder2400, and the operation amount correcting signal 3007 used foroptimizing the operation amount is not directly added to the operationamount basic signal 2003, but the correcting signal 3007 is displayed inadvance on a CRT display 2800 through a display controller 2850 so thatthe displayed contents can be certified by a user. Further, the presentembodiment is arranged in such a way that an enabling button 2700 isprovided, a user turns the button 2700 on to input an addition enablingcommand to an input controller 2750 thereby to make an enabling commandsignal 2751 generated, and the switch 2900 is turned on by the signal2751 thereby to input the correcting signal 3007 to the adder 2400.

FIG. 11 is a block diagram showing an embodiment which is arranged insuch a way that in FIG. 1, a process switch 2900 is provided before theprocess response predicting sections 2100 and 3100, and the modelcharacteristic parameter correcting signal 4003 is not directly added toboth the predicting sections 2100 and 3100, but the correcting signal4003 is displayed in advance on a CRT display 2800 through a displaycontroller 2850 so that the displayed contents can be certified by auser. Further, the present embodiment is arranged in such a way that anenabling button 2700 is provided, a user turns the button 2700 on toinput a correction enabling command to an input controller 2750 therebyto make an enabling command signal 2751 generated, and the switch 2900is turned on by the signal 2751 thereby to apply the correcting signal4003 to both the predicting sections 2100 and 3100.

FIG. 12 is a block diagram showing an embodiment which is arranged insuch a way that in FIG. 1, a switch 2900 is provided before theoperation amount calculating sections 2300 and 3300, and the controlparameter correcting signal 4005 is not directly applied to both thecalculating sections 2300 and 3300, but the correcting signal 4005 isdisplayed in advance on a CRT display 2800 through a display controller2850 so that the displayed contents can be certified by a user Further,the present embodiment is arranged in such a way that an enabling button2700 is provided, a user turns the button 2700 on to input a correctionenabling command to an input controller 2750 thereby to make an enablingcommand signal 2751 generated, and the switch 2900 is turned on by thesignal 2751 to thereby to apply the correcting signal 4005 to bothcalculating sections 2300 and 3300.

Incidentally, in the case where a user certifies the displayed contentsto performs the operation, the operation amount including the correctionamount may be also displayed on a display.

As set forth hereinabove, according to the present invention, there areprovided the following effects.

(1) Since an optimal operation amount is determined and output withoutgiving any disturbance to the plant, it is possible to control the plantstably and suitably while remaining faithful to the target. This effectgreatly depends on the operation amount optimizing section.

(2) Since, even if the characteristics of the process are changed, thedesired control characteristics can be maintained, it is possible toperform various applications such as the wide operation from thestarting of the plant to the load operation, various kinds of fueloperations and the improvements in the performance of the aged plant.This effect depends on the optimization of repetition by the predictivejudgement of the control result by the predicting model, and theparameter tuning section.

(3) Since the optimal tuning of the control section can be automaticallyperformed corresponding to the characteristic change of the process, itis possible to improve the efficiency of the maintenance. This effectlargely depends on the parameter tuning section.

(4) By only incorporating the qualitative knowledge and the ability of aveteran operator and a specialist in the form of a knowledge base andthe like, the control performance of the plant can be improved to thedesired characteristics in a self-growth manner. This effect dependsmainly upon the parameter tuning section.

(5) After the operation amount correcting signal is certified by the CRTdisplay before the operation amount basic signal is corrected with thecorrection signal, the correction can be carried out by the command froma user. Moreover, by perceiving the nonconformance and the like of thecorrection value and the operation in advance, it is possible to improvethe reliability of the control operation.

(6) After the correction signal is certified by the CRT display beforethe characteristic parameters of the process response prediction arecorrected, the correction can be carried out by the command from a user.Moreover, by perceiving the nonconformance and the like of thecorrection value and the operation in advance, it is possible to improvethe reliability of the control operation.

(7) After the correcting signal is certified by the CRT display beforethe control parameters of the operation amount calculating section arecorrected, the correction can be carried out by the command from a user.Moreover, by perceiving the nonconformance and the like of thecorrection value and the operation in advance, it is possible to improvethe reliability of the control operation.

Many different embodiments of the present invention may be constructedwithout departing from the spirit and scope of the invention. It shouldbe understood that the present invention is not limited to the specificembodiments described in this specification. To the contrary, thepresent invention is intended to cover various modifications andequivalent arrangements included within the spirit and scope of theclaims.

What is claimed is:
 1. A process controller for controlling a process toa target state using a difference between a predicted value of a controlamount of the process for a fixed period of time and a target value ofthe control amount of the process at the end of the fixed period oftime, said process controller comprising:target value setting means forgiving a target value of a process control amount after the fixed periodof time has elapsed; control means comprising: first predicting meansfor predicting a response of the process for the fixed period of time onthe basis of the present value of the control amount of the process andan operation amount, and basic operation amount calculation means forcalculating the operation amount on the basis of a differences between afirst prediction obtained by said first prediction means and the targetvalue; and operation amount optimization means comprising: secondprediction means for predicting a response of the process for the fixedperiod of time on the basis of the present value of the control amountof the process and the basic operation amount, operation amountcalculation means for calculating the operation amount on the basis of adifference between a second prediction obtained by said secondprediction means and the target value, and means for performing controlin such a way that the second prediction and a predetermined evaluationcriterion are compared with each other to be evaluated, the presentvalue of the process control amount and the calculation result of theoperation amount are repeatedly applied to said second prediction meansuntil the evaluation criterion is satisfied, a correction quantity forthe operation amount when the second prediction result satisfies theevaluation criterion is added to the basic operation amount, and theaddition result is output to the process.
 2. A process controlleraccording to claim 1, further comprising model correction means forcorrecting characteristic parameters of a model of said first predictionmeans in said control means and a model of said, second prediction meansin said operation amount optimizing means, on the basis of a result ofevaluation of an error between a prediction of the process response toan operation amount signal and an actual response signal of the plant.3. A process controller according to claim 1, further comprisingparameter tuning means for correcting control parameters of said basicoperation amount calculation means in said control means and saidoperation amount calculation means in said operation amount optimizingmeans, on the basis of a result of evaluating a relationship betweenactual response characteristics of the process and the control targetvalue by a predetermined performance index.
 4. A process controlleraccording to claim 1, further comprising model correction mean andparameter tuning means.
 5. A process controller according to claim 2,wherein said model correction means stores therein correction rules ofthe characteristic parameters of the model in the form of a knowledgebase, applies evaluation results of the model error to membershipfunctions to classify the evaluation results thereinto, and is made tooperate by the fuzzy reasoning employing rules corresponding to theclassified result.
 6. A process controller according to claim 3, whereinsaid parameter tuning means is made to operate by a neural network to beadapted to calculate setting values of the control parameters at a highspeed on the basis of a result of learning in advance target settingvalues of the plant, response characteristic evaluation indexes andstate quantities of the process by receiving them as its input.
 7. Aprocess controller according to claim 6, wherein a fuzzy reasoningfunction and a learning function are additionally provided in such a waythat determination rules of correction quantities of the characteristicparameters are stored in the form of a knowledge base, the responsecharacteristics of the process are applied to membership functions to beclassified thereinto, correction quantities of parameter setting valuesare obtained using rules corresponding to the classified result by thefuzzy interference, the control parameter setting values are correctedby the correction quantities, and the neural network is made to carryout the learning with the corrected values being treated as a controlparameter setting teacher signal.
 8. A process controller forcontrolling a state of a process to a target state using a differencebetween a predicted value of a control amount of the process for a fixedperiod of time and a target value of the control amount of the processat the end of the fixed period of time, said process controllercomprises:means for giving a target value of the control amount of theprocess; means for predicting a response of the process for the fixedperiod of time on the basis of the current value of the control amountof the process and an operation amount; means for calculating theoperation amount on the basis of a difference between the predictedresult and the target value; means for comparing the predicted resultand a predetermined evaluation criterion with each other; means forrepeatedly applying the calculation result of the present value of theprocess controlled variable and the operation amount of the predictedresult until the evaluation criterion is satisfied; means forcalculating a correction quantity for the operation amount when thepredicted result satisfies the evaluation criterion; and display meansfor displaying the correction quantity for optimizing the operationamount, wherein the correction operation is carried out by a correctionenabling command from an operator.
 9. A process controller forcontroller a state of a process to a target state using a differencebetween a predicted value of a control amount of the process for a fixedperiod of time and a target value of the control amount of the processat the end of the fixed period of time, and process controllercomprises:means for giving a target value of the controlled variable ofthe process; means for predicting a response of the process for thefixed period of time on the basis of the present value of the controlamount of the process and an operation amount; means for calculating theoperation amount on the basis of a difference between the predictedresult and the target value; means for comparing the predicted resultand a predetermined evaluation criterion with each other; means forrepeatedly applying the calculation result of the present value of theprocess controlled amount and the operation amount of the predictedresult until the evaluation criterion is satisfied; means forcalculating a correction quantity for the operation amount when thepredicted result satisfies the evaluation criterion; means forcorrecting characteristic parameters of a model of prediction means onthe basis of a result of evaluation of an error between a predictionresult of the process response to the operation amount and an actualresponse result of the plant; and display means for displaying, beforecorrecting the characteristic parameters of said prediction means, thecorrection amount, and wherein the correction operation is carried outby a correction enabling command from an operator.
 10. A processcontroller for controlling a state of a process to a target state usinga difference between a predicted value of a controlled variable of theprocess for a fixed period of time and a target value of the controlamount of the process at the said process controller comprises:parametertuning means for giving a target value of the control amount of theprocess; means for predicting a response of the process up for the fixedperiod of time on the basis of the current value of the control amountof the process and an operation amount; means for calculating theoperation amount on the basis of a difference between the predictedresult and the target value; means for comparing the predicted resultand a predetermined evaluation criterion with each other; means forrepeatedly applying the calculation result of the present value of theprocess control amount and the operation amount to the predicted resultuntil the evaluation criterion is satisfied; means for calculating acorrection quantity for the operation amount when the predicted resultsatisfies the evaluation criterion; means for correcting controlparameters on the basis of a result of evaluating a relationship betweenactual response characteristics of the process and the control targetvalue by a predetermined performance index; and display means fordisplaying, before correcting the control parameters, the correctionamount of the control parameters, and wherein the correction operationis carried out by a correction enabling command for an operator.
 11. Aprocess controller for controlling a state of a process to a targetstate using a difference between a predicted value of a control amountof the process for a fixed period of time and a target value of thecontrol amount of the process at the end of the fixed period of time,said process controller comprises:means for giving a target value of thecontrol amount of the process for the fixed period of time; means forpredicting a response to the process for the fixed period of time on thebasis of the current value of the control amount of the process and anoperation amount; means for calculating the operation amount on thebasis of a difference between the predicted result and the target value;means for comparing the predicted result and a predetermined evaluationcriterion with each other; means for repeatedly applying the calculationresult of the present value of the process control amount and theoperation amount to the predicted result until the evaluation criterionis satisfied; means for calculating a correction quantity for theoperation amount when the predicted result satisfies the evaluationcriterion; and display means for displaying the operation amount havingthe correction amount for optimizing the operation amount, and whereinthe correction operation is carried out by a correction enabling commandfor an operator.
 12. A process controller for controlling a state of aprocess to a target state using a difference between a predicted valueof a control amount of the process for a fixed period of time and atarget value of the control amount of the process at the end of thefixed period of time, said process controller comprises:means for givinga target value of the control amount of the process; means forpredicting a response of the process for the fixed period of time on thebasis of the current value of the control amount of the process and anoperation amount; means for calculating the operation amount on thebasis of a difference between the predicted result and the target value;means for comparing the predicted result and a predetermined evaluationcriterion with each other; means for repeatedly applying the calculationresult of the present value of the process control amount and theoperation amount to the prediction until the evaluation criterion issatisfied; means for calculating a correction quantity for the operationamount when the predicted result satisfies the evaluation criterion;means for correcting characteristic parameters of a model of predictionmeans on the basis of a result of evaluation of an error between aprediction result of the process response to the operation amount and anactual response result of the plant; and display means for displaying,before correcting the control parameters of said prediction means, thecorrection amount including therein the correction amount, and whereinthe correction operation is carried out by a correction enabling commandfor an operator.
 13. A process controller for controlling a state of aprocess to a target state using a difference between a predicted valueof a control amount of the process for a fixed period of time and atarget value of the control amount of the process at the end of thefixed period of time, said process controller comprises:parameter tuningmeans for giving a target value of the control amount of the process forthe fixed period of time; means for predicting a response of the processfor the fixed period of time on the basis of the current value of thecontrol amount of the process and an operation amount; means forcalculating the operation amount on the basis of a difference betweenthe resultant predicted result and the target value; means for comparingthe predicted result and a predetermined evaluation criterion with eachother; means for repeatedly applying the calculation result of thepresent value of the process control amount and the operation amount tothe predicted result until the evaluation criterion is satisfied; meansfor calculating a correction quantity for the operation amount when thepredicted result satisfies the evaluation criterion; means forcorrecting control parameters on the basis of a result of evaluating arelationship between actual response characteristics of the process andthe control target value by a predetermined evaluation index; anddisplay means for displaying, before correcting the control parameters,the operation amount including therein the correction amounts of thecontrol parameters, and wherein the correction operation is carried outby a correction enabling command for an operator.